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Trypsin is the protease of choice in bottom-up proteomics. However, its application can be limited by the amino acid composition of target proteins and the pH of the digestion solution. In this study we characterize ProAlanase, a protease from the fungus Aspergillus niger that cleaves primarily on the C-terminal side of proline and alanine residues. ProAlanase achieves high proteolytic activity and specificity when digestion is carried out at acidic pH (1.5) for relatively short (2 h) time periods. To elucidate the potential of ProAlanase in proteomics applications, we conducted a series of investigations comprising comparative multi-enzymatic profiling of a human cell line proteome, histone PTM analysis, ancient bone protein identification, phosphosite mapping and de novo sequencing of a proline-rich protein and disulfide bond mapping in mAb. The results demonstrate that ProAlanase is highly suitable for proteomics analysis of the arginine- and lysine-rich histones, enabling high sequence coverage of multiple histone family members. It also facilitates an efficient digestion of bone collagen thanks to the cleavage at the C terminus of hydroxyproline which is highly prevalent in collagen. This allows to identify complementary proteins in ProAlanase- and trypsin-digested ancient bone samples, as well as to increase sequence coverage of noncollagenous proteins. Moreover, digestion with ProAlanase improves protein sequence coverage and phosphosite localization for the proline-rich protein Notch3 intracellular domain (N3ICD). Furthermore, we achieve a nearly complete coverage of N3ICD protein by de novo sequencing using the combination of ProAlanase and tryptic peptides. Finally, we demonstrate that ProAlanase is efficient in disulfide bond mapping, showing high coverage of disulfide-containing regions in a nonreduced mAb.We introduce a systematic method of approximating finite-time transition probabilities for continuous-time insertion-deletion models on sequences. The method uses automata theory to describe the action of an infinitesimal evolutionary generator on a probability distribution over alignments, where both the generator and the alignment distribution can be represented by pair hidden Markov models (HMMs). In general, combining HMMs in this way induces a multiplication of their state spaces; to control this, we introduce a coarse-graining operation to keep the state space at a constant size. This leads naturally to ordinary differential equations for the evolution of the transition probabilities of the approximating pair HMM. The TKF91 model emerges as an exact solution to these equations for the special case of single-residue indels. For the more general case of multiple-residue indels, the equations can be solved by numerical integration. Using simulated data, we show that the resulting distribution over alignments, when compared to previous approximations, is a better fit over a broader range of parameters. We also propose a related approach to develop differential equations for sufficient statistics to estimate the underlying instantaneous indel rates by expectation maximization. Our code and data are available at https//github.com/ihh/trajectory-likelihood.Immunity to viruses requires an array of critical cellular proteins that include IFN regulatory factor 3 (IRF3). Consequently, most viruses that infect vertebrates encode proteins that interfere with IRF3 activation. This review describes the cellular pathways linked to IRF3 activation and where those pathways are targeted by human viral pathogens. Moreover, key regulatory pathways that control IRF3 are discussed. Besides viral infections, IRF3 is also involved in resistance to some bacterial infections, in anticancer immunity, and in anticancer therapies involving DNA damage agents. A recent finding shows that IRF3 is needed for T cell effector functions that are involved in anticancer immunity and also in T cell autoimmune diseases. In contrast, unregulated IRF3 activity is clearly not beneficial, considering it is implicated in certain interferonopathies, in which heightened IRF3 activity leads to IFN-β-induced disease. Therefore, IRF3 is involved largely in maintaining health but sometimes contributing to disease.Sensitive and specific severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serologic assays are needed to inform diagnostic, therapeutic, and public health decision-making. We evaluated three commercial serologic assays as stand-alone tests and as components of two-test algorithms. Two nucleocapsid antibody tests (Abbott IgG and Roche total antibody) and one spike protein antibody test (DiaSorin IgG) were included. We assessed sensitivity using 128 serum samples from symptomatic PCR-confirmed coronavirus disease 2019 (COVID-19)-infected patients and specificity using 1,204 samples submitted for routine serology prior to COVID-19's emergence, plus 64 pandemic-era samples from SARS-CoV-2 PCR-negative patients with respiratory symptoms. Assays were evaluated as stand-alone tests and as components of a two-test algorithm in which positive results obtained using one assay were verified using a second assay. The two nucleocapsid antibody tests were more sensitive than the spike protein antibody test overall (70% and 70% versus 57%; P ≤ 0.003), with pronounced differences observed using samples collected 7 to 14 days after symptom onset. [https://www.selleckchem.com/products/Decitabine.html find more] All three assays were comparably sensitive (≥89%; P ≥ 0.13) using samples collected &gt;14 days after symptom onset. Specificity was higher using the nucleocapsid antibody tests (99.3% and 99.7%) than using the spike protein antibody test (97.8%; P ≤ 0.002). When any two assays were paired in a two-test algorithm, the specificity was 99.9% (P  less then  0.0001 to 0.25 compared with the individual assays), and the positive predictive value (PPV) improved substantially, with a minimal effect on the negative predictive value (NPV). In conclusion, two nucleocapsid antibody tests outperformed a spike protein antibody test. Pairing two different serologic tests in a two-test algorithm improves the PPV, compared with the individual assays alone, while maintaining the NPV.